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Details for: "Perturbetd linear cable mass problem of order 20"

Steps for local reproduction
Name: Perturbetd linear cable mass problem of order 20 (Key: CO147)
Path: ackrep_data/system_models/compleib_models/CM4_IS View on GitHub
Type: system_model
Short Description: CM4_IS To CM1_IS corresponding example of order 240, sd=4.79125e-002
Created: 2022-10-10 15:55:39
Compatible Environment: default_conda_environment (Key: CDAMA)
Source Code [ / ] simulation.py 
# This file was autogenerated from the template: simulation.py.template (2022-10-10 15:55:47).

import numpy as np
import system_model
from scipy.integrate import solve_ivp, odeint

from ackrep_core import ResultContainer
from ackrep_core.system_model_management import save_plot_in_dir
import matplotlib.pyplot as plt
import os
from ipydex import Container

# link to documentation with examples: https://ackrep-doc.readthedocs.io/en/latest/devdoc/contributing_data.html


def simulate():
    """
    simulate the system model with scipy.integrate.solve_ivp

    :return: result of solve_ivp, might contains input function
    """

    model = system_model.Model()

    rhs_xx_pp_symb = model.get_rhs_symbolic()
    rhs = model.get_rhs_func()

    # initial state values
    xx0 = np.ones(model.sys_dim)

    t_end = 10
    tt = np.linspace(0, t_end, 1000)

    simulation_data = solve_ivp(rhs, (0, t_end), xx0, t_eval=tt)

    # using odeint for models with large state vectors
    # res = odeint(rhs, y0=xx0, t=tt, tfirst=True)
    # simulation_data = Container()
    # simulation_data.y = res.transpose()
    # simulation_data.t = tt

    # postprocessing: calc output
    ny = 2
    C = model.get_parameter_value("C")
    D21 = model.get_parameter_value("D21")
    output = np.zeros((ny, len(tt)))
    for i in range(len(tt)):
        output[:,i] = np.matmul(C, simulation_data.y[:,i]) # + np.matmul(D21, w)
    simulation_data.output = output

    save_plot(simulation_data)

    return simulation_data


def save_plot(simulation_data):
    """
    plot your data and save the plot
    access to data via: simulation_data.t   array of time values
                        simulation_data.y   array of data components
                        simulation_data.uu  array of input values

    :param simulation_data: simulation_data of system_model
    :return: None
    """

    for i in range(simulation_data.output.shape[0]):
        plt.plot(simulation_data.t, simulation_data.output[i], label=f"$y_{i}$")

    plt.legend()
    plt.tight_layout()

    save_plot_in_dir()


def evaluate_simulation(simulation_data):
    """
    assert that the simulation results are as expected

    :param simulation_data: simulation_data of system_model
    :return:
    """
    expected_final_state = np.zeros(240)

    rc = ResultContainer(score=1.0)
    simulated_final_state = simulation_data.y[:, -1]
    rc.final_state_errors = [
        simulated_final_state[i] - expected_final_state[i] for i in np.arange(0, len(simulated_final_state))
    ]
    rc.success = np.allclose(expected_final_state, simulated_final_state, rtol=0, atol=1e-2)

    return rc
system_model.py 
# This file was autogenerated from the template: system_model.py.template (2022-10-10 15:55:47).

import sympy as sp
import numpy as np
import symbtools as st
import importlib
import sys, os
#from ipydex import IPS, activate_ips_on_exception  

from ackrep_core.system_model_management import GenericModel, import_parameters

# Import parameter_file
params = import_parameters()


#link to documentation with examples: https://ackrep-doc.readthedocs.io/en/latest/devdoc/contributing_data.html


class Model(GenericModel): 

    def initialize(self):
        """
        this function is called by the constructor of GenericModel

        :return: None
        """

        # Define number of inputs -- MODEL DEPENDENT
        self.u_dim = 1

        # Set "sys_dim" to constant value, if system dimension is constant 
        self.sys_dim = 240

        # check existence of params file
        self.has_params = True
        self.params = params
        

    # ----------- SET DEFAULT INPUT FUNCTION ---------- # 
    def uu_default_func(self):
        """
        define input function
    
        :return:(function with 2 args - t, xx_nv) default input function 
        """ 
        
        def uu_rhs(t, xx_nv):
            """
            sequence of numerical input values

            :param t:(scalar or vector) time
            :param xx_nv:(vector or array of vectors) numeric state vector
            :return:(list) numeric inputs 
            """ 
            u = np.zeros(self.u_dim)
            return u

        return uu_rhs


    # ----------- SYMBOLIC RHS FUNCTION ---------- # 

    def get_rhs_symbolic(self):
        """
        define symbolic rhs function

        :return: matrix of symbolic rhs-functions
        """
        if self.dxx_dt_symb is not None:
            return self.dxx_dt_symb

        x = self.xx_symb  
        A, B, B1, C1, C, D11, D12, D21 = self.pp_symb   # parameters
        w = np.zeros(1) # noise
        u = self.uu_symb   # inputs

        # define symbolic rhs functions
        self.dxx_dt_symb = np.matmul(A,x) + np.matmul(B1,w) + np.matmul(B,u)
        



        return self.dxx_dt_symb
parameters.py 
# This file was autogenerated from the template: parameters.py.template (2022-10-10 15:55:47).

import sys
import os
import numpy as np
import sympy as sp

import tabulate as tab


#link to documentation with examples: https://ackrep-doc.readthedocs.io/en/latest/devdoc/contributing_data.html


# set model name
model_name = 'Perturbetd linear cable mass problem of order 20'


# ---------- create symbolic parameters
A = sp.MatrixSymbol('A', 240, 240)
B = sp.MatrixSymbol('B', 240, 1)
B1 = sp.MatrixSymbol('B1', 240, 1)
C1 = sp.MatrixSymbol('C1', 3, 240)
C = sp.MatrixSymbol('C', 2, 240)
D11 = sp.MatrixSymbol('D11', 3, 1)
D12 = sp.MatrixSymbol('D12', 3, 1)
D21 = sp.MatrixSymbol('D21', 2, 1)

pp_symb = [A, B, B1, C1, C, D11, D12, D21]


# ---------- create auxiliary symbolic parameters 

# set numerical values of auxiliary parameters
# trailing "_nv" stands for "numerical value"
A_nv = sp.Matrix(np.array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
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        -1.19261138e-16,  4.45088627e-16, -1.66109337e-15,
         6.19928485e-15, -2.31360460e-14,  8.63448993e-14,
        -3.22243551e-13,  1.20262931e-12, -4.48827367e-12,
         1.67504654e-11, -6.25135878e-11,  2.33303886e-10,
        -8.70701956e-10,  3.24950394e-09, -1.21273138e-08,
         4.52597512e-08, -1.68911691e-07,  6.30387014e-07,
        -2.35263636e-06,  8.78015844e-06, -3.27679974e-05,
         1.22291831e-04, -4.56399327e-04,  1.70330548e-03,
        -6.35682258e-03,  2.37239849e-02, -8.85391168e-02,
         3.30432482e-01, -1.23319081e+00,  4.60233077e+00,
        -1.71761323e+01,  6.41021983e+01, -5.05624552e+01,
         9.63108896e-71, -3.85243558e-70,  1.44466334e-69,
        -5.39340981e-69,  2.01289759e-68, -7.51224938e-68,
         2.80360999e-67, -1.04632150e-66,  3.90492502e-66,
        -1.45733786e-65,  5.43885892e-65, -2.02980978e-64,
         7.57535324e-64, -2.82716032e-63,  1.05511060e-62,
        -3.93772635e-62,  1.46957948e-61, -5.48454528e-61,
         2.04686017e-60, -7.63898614e-60,  2.85090844e-59,
        -1.06397351e-58,  3.97080321e-58, -1.48192393e-57,
         5.53061541e-57, -2.06405377e-56,  7.70315354e-56,
        -2.87485604e-55,  1.07291088e-54, -4.00415792e-54,
         1.49437208e-53, -5.57707253e-53,  2.08139180e-52,
        -7.76785996e-52,  2.89900480e-51, -1.08192332e-50,
         4.03779281e-50, -1.50692479e-49,  5.62391988e-49,
        -2.09887547e-48,  7.83310990e-48, -2.92335641e-47,
         1.09101147e-46, -4.07171022e-46,  1.51958294e-45,
        -5.67116075e-45,  2.11650601e-44, -7.89890795e-44,
         2.94791258e-43, -1.10017595e-42,  4.10591255e-42,
        -1.53234742e-41,  5.71879844e-41, -2.13428464e-40,
         7.96525870e-40, -2.97267502e-39,  1.10941742e-38,
        -4.14040217e-38,  1.54521913e-37, -5.76683629e-37,
         2.15221261e-36, -8.03216679e-36,  2.99764546e-35,
        -1.11873651e-34,  4.17518151e-34, -1.55819895e-33,
         5.81527766e-33, -2.17029117e-32,  8.09963691e-32,
        -3.02282565e-31,  1.12813389e-30, -4.21025300e-30,
         1.57128781e-29, -5.86412594e-29,  2.18852159e-28,
        -8.16767378e-28,  3.04821735e-27, -1.13761020e-26,
         4.24561908e-26, -1.58448661e-25,  5.91338454e-25,
        -2.20690515e-24,  8.23628216e-24, -3.07382235e-23,
         1.14716612e-22, -4.28128224e-22,  1.59779628e-21,
        -5.96305691e-21,  2.22544313e-20, -8.30546685e-20,
         3.09964243e-19, -1.15680230e-18,  4.31724497e-18,
        -1.61121776e-17,  6.01314653e-17, -2.24413684e-16,
         8.37523269e-16, -3.12567939e-15,  1.16651943e-14,
        -4.35350978e-14,  1.62475197e-13, -6.06365690e-13,
         2.26298756e-12, -8.44558456e-12,  3.15193507e-11,
        -1.17631818e-10,  4.39007922e-10, -1.63839987e-09,
         6.11459156e-09, -2.28199664e-08,  8.51652739e-08,
        -3.17841129e-07,  1.18619924e-06, -4.42695584e-06,
         1.65216241e-05, -6.16595406e-05,  2.30116538e-04,
        -8.58806613e-04,  3.20510991e-03,  4.53847095e-02]]))
B_nv = sp.Matrix(np.array([[ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [-8.02590746e-69],
       [ 3.21036299e-68],
       [-1.20388612e-67],
       [ 4.49450818e-67],
       [-1.67741466e-66],
       [ 6.26020782e-66],
       [-2.33634166e-65],
       [ 8.71934587e-65],
       [-3.25410418e-64],
       [ 1.21444821e-63],
       [-4.53238244e-63],
       [ 1.69150815e-62],
       [-6.31279437e-62],
       [ 2.35596693e-61],
       [-8.79258829e-61],
       [ 3.28143862e-60],
       [-1.22464957e-59],
       [ 4.57045440e-59],
       [-1.70571680e-58],
       [ 6.36582178e-58],
       [-2.37575703e-57],
       [ 8.86644595e-57],
       [-3.30900268e-56],
       [ 1.23493661e-55],
       [-4.60884618e-55],
       [ 1.72004481e-54],
       [-6.41929462e-54],
       [ 2.39571337e-53],
       [-8.94092400e-53],
       [ 3.33679827e-52],
       [-1.24531007e-51],
       [ 4.64756044e-51],
       [-1.73449317e-50],
       [ 6.47321663e-50],
       [-2.41583734e-49],
       [ 9.01602768e-49],
       [-3.36482734e-48],
       [ 1.25577066e-47],
       [-4.68659990e-47],
       [ 1.74906289e-46],
       [-6.52759159e-46],
       [ 2.43613034e-45],
       [-9.09176222e-45],
       [ 3.39309185e-44],
       [-1.26631912e-43],
       [ 4.72596729e-43],
       [-1.76375500e-42],
       [ 6.58242329e-42],
       [-2.45659382e-41],
       [ 9.16813293e-41],
       [-3.42159379e-40],
       [ 1.27695619e-39],
       [-4.76566537e-39],
       [ 1.77857053e-38],
       [-6.63771558e-38],
       [ 2.47722918e-37],
       [-9.24514516e-37],
       [ 3.45033515e-36],
       [-1.28768261e-35],
       [ 4.80569691e-35],
       [-1.79351050e-34],
       [ 6.69347233e-34],
       [-2.49803788e-33],
       [ 9.32280429e-33],
       [-3.47931793e-32],
       [ 1.29849913e-31],
       [-4.84606472e-31],
       [ 1.80857597e-30],
       [-6.74969743e-30],
       [ 2.51902137e-29],
       [-9.40111575e-29],
       [ 3.50854416e-28],
       [-1.30940651e-27],
       [ 4.88677161e-27],
       [-1.82376799e-26],
       [ 6.80639482e-26],
       [-2.54018113e-25],
       [ 9.48008503e-25],
       [-3.53801590e-24],
       [ 1.32040551e-23],
       [-4.92782045e-23],
       [ 1.83908763e-22],
       [-6.86356847e-22],
       [ 2.56151862e-21],
       [-9.55971765e-21],
       [ 3.56773520e-20],
       [-1.33149690e-19],
       [ 4.96921409e-19],
       [-1.85453595e-18],
       [ 6.92122237e-18],
       [-2.58303536e-17],
       [ 9.64001918e-17],
       [-3.59770414e-16],
       [ 1.34268146e-15],
       [-5.01095544e-15],
       [ 1.87011403e-14],
       [-6.97936057e-14],
       [ 2.60473283e-13],
       [-9.72099525e-13],
       [ 3.62792482e-12],
       [-1.35395997e-11],
       [ 5.05304742e-11],
       [-1.88582297e-10],
       [ 7.03798713e-10],
       [-2.62661256e-09],
       [ 9.80265151e-09],
       [-3.65839935e-08],
       [ 1.36533322e-07],
       [-5.09549296e-07],
       [ 1.90166386e-06],
       [-7.09710616e-06],
       [ 2.64867608e-05],
       [-9.88499369e-05],
       [ 3.68912987e-04],
       [-1.37680201e-03],
       [ 5.13829505e-03],
       [-1.91763782e-02],
       [ 7.15672178e-02],
       [-2.67092493e-01],
       [ 9.96802754e-01]]))
B1_nv = sp.Matrix(np.array([[ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [ 0.00000000e+00],
       [-8.02590746e-69],
       [ 3.21036299e-68],
       [-1.20388612e-67],
       [ 4.49450818e-67],
       [-1.67741466e-66],
       [ 6.26020782e-66],
       [-2.33634166e-65],
       [ 8.71934587e-65],
       [-3.25410418e-64],
       [ 1.21444821e-63],
       [-4.53238244e-63],
       [ 1.69150815e-62],
       [-6.31279437e-62],
       [ 2.35596693e-61],
       [-8.79258829e-61],
       [ 3.28143862e-60],
       [-1.22464957e-59],
       [ 4.57045440e-59],
       [-1.70571680e-58],
       [ 6.36582178e-58],
       [-2.37575703e-57],
       [ 8.86644595e-57],
       [-3.30900268e-56],
       [ 1.23493661e-55],
       [-4.60884618e-55],
       [ 1.72004481e-54],
       [-6.41929462e-54],
       [ 2.39571337e-53],
       [-8.94092400e-53],
       [ 3.33679827e-52],
       [-1.24531007e-51],
       [ 4.64756044e-51],
       [-1.73449317e-50],
       [ 6.47321663e-50],
       [-2.41583734e-49],
       [ 9.01602768e-49],
       [-3.36482734e-48],
       [ 1.25577066e-47],
       [-4.68659990e-47],
       [ 1.74906289e-46],
       [-6.52759159e-46],
       [ 2.43613034e-45],
       [-9.09176222e-45],
       [ 3.39309185e-44],
       [-1.26631912e-43],
       [ 4.72596729e-43],
       [-1.76375500e-42],
       [ 6.58242329e-42],
       [-2.45659382e-41],
       [ 9.16813293e-41],
       [-3.42159379e-40],
       [ 1.27695619e-39],
       [-4.76566537e-39],
       [ 1.77857053e-38],
       [-6.63771558e-38],
       [ 2.47722918e-37],
       [-9.24514516e-37],
       [ 3.45033515e-36],
       [-1.28768261e-35],
       [ 4.80569691e-35],
       [-1.79351050e-34],
       [ 6.69347233e-34],
       [-2.49803788e-33],
       [ 9.32280429e-33],
       [-3.47931793e-32],
       [ 1.29849913e-31],
       [-4.84606472e-31],
       [ 1.80857597e-30],
       [-6.74969743e-30],
       [ 2.51902137e-29],
       [-9.40111575e-29],
       [ 3.50854416e-28],
       [-1.30940651e-27],
       [ 4.88677161e-27],
       [-1.82376799e-26],
       [ 6.80639482e-26],
       [-2.54018113e-25],
       [ 9.48008503e-25],
       [-3.53801590e-24],
       [ 1.32040551e-23],
       [-4.92782045e-23],
       [ 1.83908763e-22],
       [-6.86356847e-22],
       [ 2.56151862e-21],
       [-9.55971765e-21],
       [ 3.56773520e-20],
       [-1.33149690e-19],
       [ 4.96921409e-19],
       [-1.85453595e-18],
       [ 6.92122237e-18],
       [-2.58303536e-17],
       [ 9.64001918e-17],
       [-3.59770414e-16],
       [ 1.34268146e-15],
       [-5.01095544e-15],
       [ 1.87011403e-14],
       [-6.97936057e-14],
       [ 2.60473283e-13],
       [-9.72099525e-13],
       [ 3.62792482e-12],
       [-1.35395997e-11],
       [ 5.05304742e-11],
       [-1.88582297e-10],
       [ 7.03798713e-10],
       [-2.62661256e-09],
       [ 9.80265151e-09],
       [-3.65839935e-08],
       [ 1.36533322e-07],
       [-5.09549296e-07],
       [ 1.90166386e-06],
       [-7.09710616e-06],
       [ 2.64867608e-05],
       [-9.88499369e-05],
       [ 3.68912987e-04],
       [-1.37680201e-03],
       [ 5.13829505e-03],
       [-1.91763782e-02],
       [ 7.15672178e-02],
       [-2.67092493e-01],
       [ 9.96802754e-01]]))
C1_nv = sp.Matrix(np.array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],
       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]))
C_nv = sp.Matrix(np.array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]]))
D11_nv = sp.Matrix(np.array([[0.],
       [0.],
       [0.]]))
D12_nv = sp.Matrix(np.array([[0.        ],
       [0.        ],
       [1.22474487]]))
D21_nv = sp.Matrix(np.array([[0.],
       [1.]]))


# ---------- create symbolic parameter functions
# parameter values can be constant/fixed values OR set in relation to other parameters (for example: a = 2*b)  


# list of symbolic parameter functions
# tailing "_sf" stands for "symbolic parameter function"
pp_sf = [A_nv, B_nv, B1_nv, C1_nv, C_nv, D11_nv, D12_nv, D21_nv]


#  ---------- list for substitution
# -- entries are tuples like: (independent symbolic parameter, numerical value)
pp_subs_list = []


# OPTONAL: Dictionary which defines how certain variables shall be written
# in the table - key: Symbolic Variable, Value: LaTeX Representation/Code
# useful for example for complex variables: {Z: r"\underline{Z}"}
latex_names = {}


# ---------- Define LaTeX table

# Define table header 
# DON'T CHANGE FOLLOWING ENTRIES: "Symbol", "Value"
tabular_header = ["Symbol", "Value"]

# Define column text alignments
col_alignment = ["center", "left"]


# Define Entries of all columns before the Symbol-Column
# --- Entries need to be latex code
col_1 = [] 

# contains all lists of the columns before the "Symbol" Column
# --- Empty list, if there are no columns before the "Symbol" Column
start_columns_list = []


# Define Entries of the columns after the Value-Column
# --- Entries need to be latex code
col_4 = []

# contains all lists of columns after the FIX ENTRIES
# --- Empty list, if there are no columns after the "Value" column
end_columns_list = []
Related Problems:
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Result: Script Error.
Last Build: Checkout CI Build
Runtime: 181.1 (estimated: 10s)
Debug: 
Checking <SystemModel (pk: 39, key: CO147)> "(Perturbetd linear cable mass problem of order 20, 10s)"
12:31:24 - ackrep - INFO -   ... Creating exec-script ... 
12:31:24 - ackrep - INFO - execscript-path: /code/ackrep/ackrep_data/execscript.py
12:31:24 - ackrep - INFO -   ... running exec-script /code/ackrep/ackrep_data/execscript.py ... 
12:34:24 - ackrep - ERROR - Entity calculation reached timeout (180s).
Fail.